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Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
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Harmful fine-tuning attack poses serious safety concerns for large language models' fine-tuning-as-a-service. While existing defenses have been proposed to mitigate the issue, their performances are still far away from satisfactory, and the root cause of the problem has not been fully recovered. To this end, we in this paper show that harmful perturbation over the model weights could be a probable cause of alignment-broken. In order to attenuate the negative impact of harmful perturbation, we propose an alignment-stage solution, dubbed Booster. Technically, along with the original alignment loss, we append a loss regularizer in the alignment stage's optimization. The regularizer ensures that the model's harmful loss reduction after the simulated harmful perturbation is attenuated, thereby mitigating the subsequent fine-tuning risk. Empirical results show that Booster can effectively reduce the harmful score of the fine-tuned models while maintaining the performance of downstream tasks. Our code is available at https://github.com/git-disl/Booster.
Forward citations
Cited by 6 Pith papers
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Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning
Calibrating a null-space gate to fully cover the defender's harmful data keeps post-fine-tuning attack success at pre-release levels, but this is a coverage and calibration consequence rather than a tested defense on ...
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HyperSafe: Inference-Time Safety Recovery for Fine-Tuned Language Models
A hypernetwork maps layer-wise activation fingerprints of a fine-tuned LLM to a Safe Side Network that routes harmful prompts to refusal without editing model weights.
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Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
Vulnerability-Aware Alignment splits safety training data into fragile and robust groups, then uses group robust optimization and adversarial perturbations, cutting harmful response rates after harmful fine-tuning by ...
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MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security
MoGUv2 embeds small routers in the deeper layers of LLMs to dynamically blend a helpful variant and a refusal variant, improving safety against jailbreak and fine-tuning attacks while preserving usability.
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Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint
ProCon anchors each sample's hidden-state projection onto the LLM's initial refusal direction during instruction fine-tuning, reducing refusal-direction drift and safety risks with limited task-performance loss.
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